The OAPEN Suggestion Engine will suggest e-books based on other books with similar content. It achieves this using a trigram semantic inferecing algorithm. You can read more about the paper which started it all [here](https://liberquarterly.eu/article/view/10938).
This project uses Docker. To run the project, you will need to have Docker installed. You can find instructions for installing Docker [here](https://docs.docker.com/get-docker/). Note that on Linux, if you do not install Docker with Docker Desktop, you will have to install Docker Compose separately, instructions for which can be found [here](https://docs.docker.com/compose/install/#scenario-two-install-the-compose-plugin).
The project uses PostgreSQL as a database. You can find instructions for installing PostgreSQL [here](https://www.postgresql.org/download/).
Make sure it is running, and a database is created. Take note of the credentials and name of the database you create, you will need them for the next step.
This project is a monorepo, with multiple services that work in tandem to provide suggestions: the database, the suggestion engine, the API server, the embed script, and the web demo.
This engine is written in Python, and generates the recommendation data for users.
Our suggestion service is centered around the trigram semantic inferencing algorithm. This script should be run as a job on a cron schedule to periodically ingest new texts added to the OAPEN catalog through their API. It populates the database with pre-processed lists of suggestions for each entry in the catalog.
This is a web-app demo that can be used to query the API engine and see suggested books. This does not have to be maintained if the API is used on another site, but is useful for development and a tech demo.
